Recently, User-Generated Content (UGC) videos have gained popularity in our daily lives. However, UGC videos often suffer from poor exposure due to the limitations of photographic equipment and techniques. Therefore, Video Exposure Correction (VEC) algorithms have been proposed, Low-Light Video Enhancement (LLVE) and Over-Exposed Video Recovery (OEVR) included. Equally important to the VEC is the Video Quality Assessment (VQA). Unfortunately, almost all existing VQA models are built generally, measuring the quality of a video from a comprehensive perspective. As a result, Light-VQA, trained on LLVE-QA, is proposed for assessing LLVE. We extend the work of Light-VQA by expanding the LLVE-QA dataset into Video Exposure Correction Quality Assessment (VEC-QA) dataset with over-exposed videos and their corresponding corrected versions. In addition, we propose Light-VQA+, a VQA model specialized in assessing VEC. Light-VQA+ differs from Light-VQA mainly from the usage of the CLIP model and the vision-language guidance during the feature extraction, followed by a new module referring to the Human Visual System (HVS) for more accurate assessment. Extensive experimental results show that our model achieves the best performance against the current State-Of-The-Art (SOTA) VQA models on the VEC-QA dataset and other public datasets.
MC-CoT: A Modular Collaborative CoT Framework for Zero-shot Medical-VQA with LLM and MLLM Integration
Huazhong University of Science and Technology（华中科技大学）；Eastern Institute of Technology（宁波工程学院）；Purple Mountain Laboratories（紫金山实验室）；Alibaba Inc.（阿里巴巴集团）；Fudan University（复旦大学）
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA
National University of Singapore（新加坡国立大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；City University of Hong Kong（香港城市大学）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））
SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Hupan Lab（湖畔实验室）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
HDRAgent: An Agentic Framework for Multi-Exposure HDR Imaging
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Shenzhen Research Institute, Northwestern Polytechnical University（西北工业大学深圳研究院）；Zhejiang University（浙江大学）；Camera Group, DJI（大疆相机部门）
UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Models
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University（集成航空航天地面海洋大数据应用技术国家工程实验室，计算机科学与工程学院，西北工业大学）
INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling
University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Diagnose, Correct, and Learn from Manipulation Failures via Visual Symbols
Beihang University（北京航空航天大学）；Shanghai Innovation Institute（上海创新研究院）；Southern University of Science and Technology（南方科技大学）；Shanghai Jiao Tong University（上海交通大学）
Jailbreak Large Vision-Language Models Through Multi-Modal Linkage
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海启智研究所）；University of Chicago（芝加哥大学）
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；USC（南加州大学）；University of Michigan-Ann Arbor（密歇根大学安娜堡分校）；University of Washington（华盛顿大学）；University of Maryland（马里兰大学）；Lehigh University（理海大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）；Peking University（北京大学）；University of Rochester（罗切斯特大学）；University of California, Davis（加州大学戴维斯分校）
MixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture
Shanghai Innovation Institute（上海创新研究院）；Fudan University（复旦大学）；The Chinese University of Hong Kong（香港中文大学）；Baidu（百度）；Shanghai Academy of AI for Science（上海人工智能科学研究院）
LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography
MoE Key Lab of Artificial Intelligence（人工智能混合专家实验室）；AI Institute（人工智能研究所）；School of Computer Science（计算机科学学院）；School of Biomedical Engineering（生物医学工程学院）；School of Artificial Intelligence（人工智能学院）
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Information and Communication Engineering, Communication University of China（中国传媒大学信息与通信工程学院）；Heilongjiang Network Space Research Center（黑龙江省网络空间研究中心）
Mitigating Exposure Bias in Discriminator Guided Diffusion Models
Artificial Intelligence and Learning Systems Laboratory（人工智能与学习系统实验室）；School of Electrical and Computer Engineering（电气与计算机工程学院）；National Technical University of Athens（雅典国立技术大学）
Overcoming Language Priors for Visual Question Answering Based on Knowledge Distillation
School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Joint Laboratory of HUST and Pingan Property & Casualty Research (HPL)（华中科技大学与平安财产保险研究联合实验室）
